• Title/Summary/Keyword: Data pooling

Search Result 102, Processing Time 0.022 seconds

Application of 2007 Korean National Growth Charts: Growth Curves and Tables (2007년 한국 소아 청소년 표준 성장곡선과 표의 적용)

  • Moon, Jin Soo
    • Pediatric Gastroenterology, Hepatology & Nutrition
    • /
    • v.12 no.sup1
    • /
    • pp.1-5
    • /
    • 2009
  • 2007 Korean National Growth Charts were published by The Korean Pediatric Society and Korea Centers for Disease Control and Prevention in October, 2007. These Growth Charts are composed of Growth Curves and Tables, which are based on the principles such as pooling data of the different generations and application of LMS method. In this review, several tips of application using these new charts are summarized with points of view both in clinical and research fields.

Pointwise CNN for 3D Object Classification on Point Cloud

  • Song, Wei;Liu, Zishu;Tian, Yifei;Fong, Simon
    • Journal of Information Processing Systems
    • /
    • v.17 no.4
    • /
    • pp.787-800
    • /
    • 2021
  • Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

A Deep Learning Model for Extracting Consumer Sentiments using Recurrent Neural Network Techniques

  • Ranjan, Roop;Daniel, AK
    • International Journal of Computer Science & Network Security
    • /
    • v.21 no.8
    • /
    • pp.238-246
    • /
    • 2021
  • The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

Predictive Convolutional Networks for Learning Stream Data (스트림 데이터 학습을 위한 예측적 컨볼루션 신경망)

  • Heo, Min-Oh;Zhang, Byoung-Tak
    • KIISE Transactions on Computing Practices
    • /
    • v.22 no.11
    • /
    • pp.614-618
    • /
    • 2016
  • As information on the internet and the data from smart devices are growing, the amount of stream data is also increasing in the real world. The stream data, which is a potentially large data, requires online learnable models and algorithms. In this paper, we propose a novel class of models: predictive convolutional neural networks to be able to perform online learning. These models are designed to deal with longer patterns as the layers become higher due to layering convolutional operations: detection and max-pooling on the time axis. As a preliminary check of the concept, we chose two-month gathered GPS data sequence as an observation sequence. On learning them with the proposed method, we compared the original sequence and the regenerated sequence from the abstract information of the models. The result shows that the models can encode long-range patterns, and can generate a raw observation sequence within a low error.

Multidimensional scaling of categorical data using the partition method (분할법을 활용한 범주형자료의 다차원척도법)

  • Shin, Sang Min;Chun, Sun-Kyung;Choi, Yong-Seok
    • The Korean Journal of Applied Statistics
    • /
    • v.31 no.1
    • /
    • pp.67-75
    • /
    • 2018
  • Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

Test of Homogeneity for Intermittent Panel AR(1) Processes and Application (간헐적인 패널 1차 자기회귀과정들의 동질성 검정과 적용)

  • Lee, Sung Duck;Kim, Sun Woo;Jo, Na Rae
    • The Korean Journal of Applied Statistics
    • /
    • v.27 no.7
    • /
    • pp.1163-1170
    • /
    • 2014
  • The concepts and structure of intermittent panel time series data are introduced. We suggest a Wald test statistic for the test of homogeneity for intermittent panel first order autoregressive model and its limit distribution is derived. We consider the fitting the model with pooling data using sample mean at the time point if homogeneity for intermittent panel AR(1) is satisfied. We performed simulations to examine the limit distribution of the homogeneity test statistic for intermittent panel AR(1). In application, we fit the intermittent panel AR(1) for panel Mumps data and investigate the test of homogeneity.

Multi-parametric MRIs based assessment of Hepatocellular Carcinoma Differentiation with Multi-scale ResNet

  • Jia, Xibin;Xiao, Yujie;Yang, Dawei;Yang, Zhenghan;Lu, Chen
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.13 no.10
    • /
    • pp.5179-5196
    • /
    • 2019
  • To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

Personal Recognition Method using Coupling Image of ECG Signal (심전도 신호의 커플링 이미지를 이용한 개인 인식 방법)

  • Kim, Jin Su;Kim, Sung Huck;Pan, Sung Bum
    • Smart Media Journal
    • /
    • v.8 no.3
    • /
    • pp.62-69
    • /
    • 2019
  • Electrocardiogram (ECG) signals cannot be counterfeited and can easily acquire signals from both wrists. In this paper, we propose a method of generating a coupling image using direction information of ECG signals as well as its usage in a personal recognition method. The proposed coupling image is generated by using forward ECG signal and rotated inverse ECG signal based on R-peak, and the generated coupling image shows a unique pattern and brightness. In addition, R-peak data is increased through the ECG signal calculation of the same beat, and it is thus possible to improve the recognition performance of the individual. The generated coupling image extracts characteristics of pattern and brightness by using the proposed convolutional neural network and reduces data size by using multiple pooling layers to improve network speed. The experiment uses public ECG data of 47 people and conducts comparative experiments using five networks with top 5 performance data among the public and the proposed networks. Experimental results show that the recognition performance of the proposed network is the highest with 99.28%, confirming potential of the personal recognition.

Optimization of Pose Estimation Model based on Genetic Algorithms for Anomaly Detection in Unmanned Stores (무인점포 이상행동 인식을 위한 유전 알고리즘 기반 자세 추정 모델 최적화)

  • Sang-Hyeop Lee;Jang-Sik Park
    • Journal of the Korean Society of Industry Convergence
    • /
    • v.26 no.1
    • /
    • pp.113-119
    • /
    • 2023
  • In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

Sampling-based Super Resolution U-net for Pattern Expression of Local Areas (국소부위 패턴 표현을 위한 샘플링 기반 초해상도 U-Net)

  • Lee, Kyo-Seok;Gal, Won-Mo;Lim, Myung-Jae
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.22 no.5
    • /
    • pp.185-191
    • /
    • 2022
  • In this study, we propose a novel super-resolution neural network based on U-Net, residual neural network, and sub-pixel convolution. To prevent the loss of detailed information due to the max pooling of U-Net, we propose down-sampling and connection using sub-pixel convolution. This uses all pixels in the filter, unlike the max pooling that creates a new feature map with only the max value in the filter. As a 2×2 size filter passes, it creates a feature map consisting only of pixels in the upper left, upper right, lower left, and lower right. This makes it half the size and quadruple the number of feature maps. And we propose two methods to reduce the computation. The first uses sub-pixel convolution, which has no computation, and has better performance, instead of up-convolution. The second uses a layer that adds two feature maps instead of the connection layer of the U-Net. Experiments with a banchmark dataset show better PSNR values on all scale and benchmark datasets except for set5 data on scale 2, and well represent local area patterns.